instruct-eval VS txtinstruct

I modified declare-lab's instruct-eval scripts, add support to VLLM, AutoGPTQ (and new autoGPTQ support exllama now), and test the mmlu result. I also add support to fastllm (which can accelerate ChatGLM2-6b.The code is here https://github.com/declare-lab/instruct-eval , I'd like to hear any errors in those code.

Well, I ran it with exactly the same parameters I ran Vicuna 7b, although I ran Vicuna with llama.cpp. while PJ can only be ran with ggml (I don't have a GPU). And Vicuna looped only when temperature reached 0. Given how hard it loops, I think it is some bug with ggml. Testers claim it should be close to 7b alpaca/vicuna:https://github.com/declare-lab/flan-eval

For a list of other instruction tuned models, you can check out this benchmark here: https://github.com/declare-lab/flan-eval

Great resource! I’ve recently also benchmarked many of the popular instruction models here: https://github.com/declare-lab/flan-eval

I really dislike this approach of announcing new models that some companies have taken, they don't mention evaluation results or performance of the model, but instead talk about how "transparent", "accessible" and "supportive" these models are.

Anyway, I have benchmarked stablelm-base-alpha-3b (the open-source version, not the fine-tuned one which is under a NC license) using the MMLU benchmark and the results are rather underwhelming compared to other open source models:

* stablelm-base-alpha-3b (3B params): 25.6% average accuracy

* flan-t5-xl (3B params): 49.3% average accuracy

* flan-t5-small (80M params): 29.4% average accuracy

MMLU is just one benchmark, but based on the blog post, I don't think it will yield much better results in others. I'll leave links to the MMLU results of other proprietary[0] and open-access[1] models (results may vary by ±2% depending on the parameters used during inference).

[0]: https://paperswithcode.com/sota/multi-task-language-understa...

[1]: https://github.com/declare-lab/flan-eval/blob/main/mmlu.py#L...

I tried cromadb but had terrible performance and could not pin down the cause (likely a problem on my end). Weaviate was easy to setup and had excellent performance, this is probably what I will use in the future. Next on my list is txtinstruct, to finetune a model with data that does not change and using a vector db for everything else seems promising.

The closest thing I've seen to this is txtinstruct

txtinstruct is a framework for training instruction-tuned models

Great to see the continued release of open models. The only disappointing thing is that models keep building on CC-BY-NC licensed datasets, which severely limits their use.

Hopefully, people consider txtinstruct (https://github.com/neuml/txtinstruct) and other approaches to generate instruction-tuning datasets without the baggage.